code: def np_to_xshard(x, workers_num, prefix="x"):
      from bigdl.orca.data import XShards
  
      x = XShards.partition(x)
      if (
          math.floor(math.sqrt(math.sqrt(x.num_partitions())))
          > workers_num
      ):
          warnings.warn(
              "Too many partitions and too few workers will reduce inference performance, "
              "we recommend setting 'workers_per_node' to a large number and",
              "not larger than the number of partitions.",
              category=RuntimeWarning,
          )
  
      def transform_to_dict(train_data):
          return {prefix: train_data}
  
      return x.transform_shard(transform_to_dict)
  
imports: import torch
  import torch.nn
  import warnings
  import random
  import numpy
  from torch.utils.data import TensorDataset, DataLoader
  import numpy
  import multiprocessing
  import warnings
  import math
label_desc: Use image node_id 6 for calling a global function with example usage: np_to_xshard(x, workers_num, prefix) and returns: x, dict